Instructions to use dbmdz/bert-small-historic-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbmdz/bert-small-historic-multilingual-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbmdz/bert-small-historic-multilingual-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-small-historic-multilingual-cased") model = AutoModelForMaskedLM.from_pretrained("dbmdz/bert-small-historic-multilingual-cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aef6e2ff209887687d35e03dad5f83887390c9868222fdaffb2fcfb5f483debf
- Size of remote file:
- 119 MB
- SHA256:
- 4f5c3f9d57a3847dfc03ee9808fbf87036c9b0cbf885913cd509e55a4452b4bb
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